How does Kubernetes help an API automatically handle many more users by increasing the number of running copies of the API service?

The API service runs within one or more containers, which are encapsulated in Kubernetes Pods. A Pod is the smallest deployable unit in Kubernetes, containing the application code and its dependencies. A Kubernetes Deployment manages these Pods, declaring the desired number of identical copies, known as replicas, of the API service that should be running. This Deployment ensures that the specified number of Pods is always active and provides declarative updates. For external users to access the API, a Kubernetes Service provides a stable network endpoint, abstracting away the individual Pods. This Service automatically performs load balancing, distributing incoming user requests across all healthy running Pods of the API, regardless of how many there are or their specific network addresses. To automatically handle many more users, Kubernetes employs the Horizontal Pod Autoscaler (HPA). The HPA is a Kubernetes resource that continuously monitors specific performance metrics of the API Pods, such as their average CPU utilization, memory consumption, or custom metrics like requests per second. When the observed metrics for the API Pods exceed a predefined threshold, for example, if the average CPU usage of the Pods goes above 80%, the HPA automatically triggers a scaling action. It modifies the `replicas` field of the Deployment managing the API, increasing the number of desired copies. Kubernetes then responds to this change by creating new Pods, which are additional running instances of the API service. As these new Pods become ready, the Kubernetes Service automatically detects them and includes them in its load balancing pool. This means that incoming user traffic is now distributed across a larger number of API instances, effectively spreading the load and preventing any single instance from becoming overwhelmed, thereby allowing the API to process a significantly higher volume of user requests without performance degradation. Conversely, if the metrics indicate that the load has decreased and the average resource utilization falls below a lower threshold, the HPA will automatically reduce the number of replicas, scaling down the API service to conserve resources.